Last updated: 2024-09-13
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Knit directory: mutation_rate/
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We cleaned the real data here.
library(ggplot2)
library(gridExtra)
library(data.table)
scatter_plot_mutct <- function(data, xcol, ycol, title=NULL) {
# Fit the linear model using user-defined columns
formula <- as.formula(paste(ycol, "~", xcol, "+0"))
fit <- lm(formula, data = data)
adj_rsq <- summary(fit)$adj.r.squared # Extract the adjusted R-squared
x_values <- data[[xcol]]
y_values <- data[[ycol]]
# Calculate the LPD (Log Predictive Density)
lpd <- sum(y_values * log(x_values),na.rm = T) - sum(x_values)
ggplot(data) +
geom_point(data = data, aes_string(x = xcol, y = ycol), color = "black") +
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
annotate("text", x = Inf, y = Inf, label = paste0("Adj R-sq = ", round(adj_rsq, 3),
"\nLPD = ", round(lpd, 3)),
hjust = 1.1, vjust = 1.1, color = "blue", parse = FALSE) + # Adjust text positioning and color
labs(x = paste("Predicted mutation count:", xcol),
y = paste("Observed mutation count:", ycol),
title = title) +
theme_minimal()
}
# scatter_plot_mutct <- function(data, xcol, ycol, title=NULL) {
# # Fit the linear model using user-defined columns
# formula <- as.formula(paste(ycol, "~", xcol, "+0"))
# fit <- lm(formula, data = data)
# adj_rsq <- summary(fit)$adj.r.squared # Extract the adjusted R-squared
#
# ggplot(data) +
# geom_point(data = data, aes_string(x = xcol, y = ycol),color = "black") +
# geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
# annotate("text", x = Inf, y = Inf, label = paste0("Adj R-sq = ", round(adj_rsq, 3)),
# hjust = 1.1, vjust = 1.1, color = "blue", parse = FALSE) + # Adjust text positioning and color
# labs(x = paste("Predicted mutation count:", xcol),
# y = paste("Observed mutation count:", ycol),
# title = title) +
# theme_minimal()
#
# }
plot_scatter_randeff <- function(data) {
ggplot(data = df_compare) +aes(x=seg_est,y=chr_est) +
geom_point() +
labs(x = "random effects estimated from partitioned segments",
y= "random effects estimated from whole chromosome") +
geom_abline(slope = 1, intercept = 0, col="red") +
theme_minimal()
}
plot_randeff_genome <- function(data) {
ggplot(df_compare, aes(x = window_start/1000000)) +
geom_line(aes(y = seg_est, color = "estimated from partitioned segments"), alpha = 0.4) +
geom_line(aes(y = chr_est, color = "estimated from whole chr"), alpha = 0.3) +
geom_line(aes(y = fold_change, color = "observed fold change"), alpha = 0.1) +
labs(x = "gemonic position (mb)",
y = "estimated random effects") +
scale_color_manual(name = "Group",
values = c("estimated from partitioned segments" = "blue",
"estimated from whole chr" = "green", "observed fold change" = "red"),
labels = c("estimated from partitioned segments","estimated from whole chr", "observed fold change")) +
theme_minimal()
}
plot_randeff_windows <- function(data) {
ggplot(data, aes(x = Window_Start/1000000)) +
geom_line(aes(y = randeff_est, color = "Estimated random effect"), alpha = 0.4) +
geom_line(aes(y = rl_rescaled_sum, color = "Roulette predicted mutation count"), alpha = 0.3) +
geom_line(aes(y = obs_sum, color = "Observed de novo mutations"), alpha = 0.1) +
labs(x = "Genomic position (Mb)",
y = "Estimated random effects") +
scale_color_manual(name = "Group",
values = c("Estimated random effect" = "blue",
"Roulette predicted mutation count" = "green",
"Observed de novo mutations" = "red")) +
guides(color = guide_legend(override.aes = list(alpha = 1))) +
theme_minimal()
}
fit = ebps(df_seg_non0$obs_sum,df_seg_non0$rl_rescaled_sum,smooth_control = list(wave_trans='ndwt',ndwt_method='smash'), general_control = list(verbose=T,printevery=1, maxiter=50))
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
i Please use tidy evaluation idioms with `aes()`.
i See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
print("fitting output example")
[1] "fitting output example"
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/fit_example_100kb_seg1.rdata")
str(fit)
List of 5
$ posterior :List of 4
..$ mean : num [1:1198] 0.239 0.238 0.253 1.41 1.976 ...
..$ mean_log : num [1:1198] -1.699 -1.684 -1.605 0.291 0.64 ...
..$ mean_smooth : num [1:1198] 0.356 0.41 0.514 0.716 0.95 ...
..$ mean_log_smooth: num [1:1198] -1.034 -0.892 -0.665 -0.335 -0.051 ...
$ log_likelihood: NULL
$ elbo_trace : num [1:8] -Inf -11561 -11518 -11504 -11499 ...
$ fitted_g :List of 2
..$ sigma2 : num 0.868
..$ sigma2_trace: num [1:8] 1.369 1.235 1.118 1.031 0.968 ...
$ run_time : 'difftime' num 61.0477244853973
..- attr(*, "units")= chr "secs"
ggplot(data, aes(x = randeff_est)) +
geom_histogram(binwidth = 0.5, fill = "skyblue", color = "black") +
labs(title = "Histogram of estimated random effects ",
x = "Estimated random effects ") +
geom_vline(xintercept = 1, color = "red", linetype = "dashed", size = 1.2) +
annotate("text", x = 1, y = Inf, label = "x = 1", vjust = 2, color = "red")
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
i Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

We plot some regions to check the results.
print("Windows with large estimated random effects")
[1] "Windows with large estimated random effects"
plot_data <- data[data$Window %in% c(400:500),]
plot_randeff_windows(plot_data)

print("Windows with small estimated random effects")
[1] "Windows with small estimated random effects"
plot_data <- data[data$Window %in% c(750:850),]
plot_randeff_windows(plot_data)

plot_data <- data[data$Window %in% c(1850:2100),]
plot_randeff_windows(plot_data)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt_smooth.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

ggplot(data, aes(x = randeff_est)) +
geom_histogram(binwidth = 0.5, fill = "skyblue", color = "black") +
labs(title = "Histogram of estimated random effects ",
x = "Estimated random effects ") +
geom_vline(xintercept = 1, color = "red", linetype = "dashed", size = 1.2) +
annotate("text", x = 1, y = Inf, label = "x = 1", vjust = 2, color = "red")

| Version | Author | Date |
|---|---|---|
| 1cfb705 | XSun | 2024-08-23 |
We plot some regions to check the results.
print("Windows with large estimated random effects")
[1] "Windows with large estimated random effects"
plot_data <- data[data$Window %in% c(400:500),]
plot_randeff_windows(plot_data)

| Version | Author | Date |
|---|---|---|
| 1cfb705 | XSun | 2024-08-23 |
print("Windows with small estimated random effects")
[1] "Windows with small estimated random effects"
plot_data <- data[data$Window %in% c(750:850),]
plot_randeff_windows(plot_data)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
plot_data <- data[data$Window %in% c(1850:2100),]
plot_randeff_windows(plot_data)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt_gammaonly.rdata")
sprintf("estimated alpha = %s", unique(df_per_window_data$alpha_est))
[1] "estimated alpha = 0.770703125"
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "Gamma prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
ggplot(data, aes(x = randeff_est)) +
geom_histogram(binwidth = 0.5, fill = "skyblue", color = "black") +
labs(title = "Histogram of estimated random effects",
x = "Estimated random effects") +
geom_vline(xintercept = 1, color = "red", linetype = "dashed", size = 1.2) +
annotate("text", x = 1, y = Inf, label = "x = 1", vjust = 2, color = "red")

We plot some regions to check the results.
print("Windows with large estimated random effects")
[1] "Windows with large estimated random effects"
plot_data <- data[data$Window %in% c(400:500),]
plot_randeff_windows(plot_data)

print("Windows with small estimated random effects")
[1] "Windows with small estimated random effects"
plot_data <- data[data$Window %in% c(750:850),]
plot_randeff_windows(plot_data)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
plot_data <- data[data$Window %in% c(1850:2100),]
plot_randeff_windows(plot_data)

We partition each window into 1kb bins, use odd bins to train the local effect, and test on the even bins
fit = ebps(df_seg_non0$obs_train,df_seg_non0$rl_rescaled_train,smooth_control = list(wave_trans='ndwt',ndwt_method='smash'), general_control = list(verbose=T,printevery=1, maxiter=50))
df_per_window_data$test_pred <- df_per_window_data$randeff_est_train*df_per_window_data$rl_rescaled_test
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_halftt_smooth.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_halftt_gammaonly.rdata")
sprintf("estimated alpha = %s", unique(df_per_window_data$alpha_est))
[1] "estimated alpha = 0.501171875"
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "Gamma prediction prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 6d64883 | XSun | 2024-09-04 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_halftt_smooth.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| 1cfb705 | XSun | 2024-08-23 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_halftt_gammaonly.rdata")
sprintf("estimated alpha = %s", unique(df_per_window_data$alpha_est))
[1] "estimated alpha = 0.649609375"
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "Gamma prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_halftt_smooth.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| d7785e1 | XSun | 2024-08-30 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_halftt_gammaonly.rdata")
sprintf("estimated alpha = %s", unique(df_per_window_data$alpha_est))
[1] "estimated alpha = 0.691015625"
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "Gamma prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| d7785e1 | XSun | 2024-08-30 |
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

| Version | Author | Date |
|---|---|---|
| d7785e1 | XSun | 2024-08-30 |
There are some windows with large observed mutation rate on testing set, but their expected rates are very low.
data_large <- data[order(data$obs_test,decreasing = T),][1:5,]
data_large <- data_large[order(data_large$obs_test, decreasing = T),]
colnames(data_large)[c(7,10,13,16)] <- c("rl_rescaled_wholewindow","obs_wholewindow","foldchange_wholewindow","randeff_est_wholewindow")
DT::datatable(data_large,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','windows with large observed mutation rate on testing set, but low expected rates'),options = list(pageLength = 10) )
We look into the window 2254. We varied the bin sizes (500bp, 1kb, 2kb) and checked how this window is divided.
obs <- fread("/project/xinhe/xsun/mutation_rate/data/trost_denovo/processed/alltype.mssng.bed")
colnames(obs) <- c("CHROM","START","REF","ALT")
obs_window <- obs[obs$START > 225310007 & obs$START < 225410006 & obs$CHROM ==1, ]
start_window <- 225310007
end_window <- 225410006
bin_sizes <- c(100, 500, 1000, 2000)
# Function to assign training or testing based on bin size
assign_bin_varying_size <- function(start_position, bin_size) {
bins <- seq(start_window, end_window, by = bin_size)
bin_index <- findInterval(start_position, bins)
if (bin_index %% 2 == 1) {
return("training")
} else {
return("testing")
}
}
# Loop over the different bin sizes and add a new column for each
for (bin_size in bin_sizes) {
column_name <- paste0("set_", bin_size)
obs_window[[column_name]] <- sapply(obs_window$START, assign_bin_varying_size, bin_size = bin_size)
}
DT::datatable(obs_window,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','how the window is divided when bin size varies'),options = list(pageLength = 10) )
sprintf("bin size 100bp, number of training= %s, number of testing = %s",sum(obs_window$set_100 =="training"),sum(obs_window$set_100 =="testing"))
[1] "bin size 100bp, number of training= 22, number of testing = 10"
sprintf("bin size 500bp, number of training= %s, number of testing = %s",sum(obs_window$set_500 =="training"),sum(obs_window$set_500 =="testing"))
[1] "bin size 500bp, number of training= 21, number of testing = 11"
sprintf("bin size 1000bp, number of training= %s, number of testing = %s",sum(obs_window$set_1000 =="training"),sum(obs_window$set_1000 =="testing"))
[1] "bin size 1000bp, number of training= 4, number of testing = 28"
sprintf("bin size 2000bp, number of training= %s, number of testing = %s",sum(obs_window$set_2000 =="training"),sum(obs_window$set_2000 =="testing"))
[1] "bin size 2000bp, number of training= 27, number of testing = 5"
We also look into the window 315.
obs_window <- obs[obs$START > 31410007 & obs$START < 31510006 & obs$CHROM ==1, ]
start_window <- 31410007
end_window <- 31510006
bin_sizes <- c(100, 500, 1000, 2000)
# Function to assign training or testing based on bin size
assign_bin_varying_size <- function(start_position, bin_size) {
bins <- seq(start_window, end_window, by = bin_size)
bin_index <- findInterval(start_position, bins)
if (bin_index %% 2 == 1) {
return("training")
} else {
return("testing")
}
}
# Loop over the different bin sizes and add a new column for each
for (bin_size in bin_sizes) {
column_name <- paste0("set_", bin_size)
obs_window[[column_name]] <- sapply(obs_window$START, assign_bin_varying_size, bin_size = bin_size)
}
DT::datatable(obs_window,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','how the window is divided when bin size varies'),options = list(pageLength = 10) )
sprintf("bin size 100bp, number of training= %s, number of testing = %s",sum(obs_window$set_100 =="training"),sum(obs_window$set_100 =="testing"))
[1] "bin size 100bp, number of training= 29, number of testing = 3"
sprintf("bin size 500bp, number of training= %s, number of testing = %s",sum(obs_window$set_500 =="training"),sum(obs_window$set_500 =="testing"))
[1] "bin size 500bp, number of training= 7, number of testing = 25"
sprintf("bin size 1000bp, number of training= %s, number of testing = %s",sum(obs_window$set_1000 =="training"),sum(obs_window$set_1000 =="testing"))
[1] "bin size 1000bp, number of training= 8, number of testing = 24"
sprintf("bin size 2000bp, number of training= %s, number of testing = %s",sum(obs_window$set_2000 =="training"),sum(obs_window$set_2000 =="testing"))
[1] "bin size 2000bp, number of training= 25, number of testing = 7"
There are also some windows having high predicted rates but low observed mutations on testing set.
data_small <- data[order(data$test_pred,decreasing = T),][1:10,]
colnames(data_small)[c(7,10,13,16)] <- c("rl_rescaled_wholewindow","obs_wholewindow","foldchange_wholewindow","randeff_est_wholewindow")
DT::datatable(data_small,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','windows with large expected rates'),options = list(pageLength = 10) )
We look into the window 454.
obs_window <- obs[obs$START > 45310007 & obs$START < 45410006 & obs$CHROM ==1, ]
start_window <- 45310007
end_window <- 45410006
bin_sizes <- c(500, 1000, 2000)
# Function to assign training or testing based on bin size
assign_bin_varying_size <- function(start_position, bin_size) {
bins <- seq(start_window, end_window, by = bin_size)
bin_index <- findInterval(start_position, bins)
if (bin_index %% 2 == 1) {
return("training")
} else {
return("testing")
}
}
# Loop over the different bin sizes and add a new column for each
for (bin_size in bin_sizes) {
column_name <- paste0("set_", bin_size)
obs_window[[column_name]] <- sapply(obs_window$START, assign_bin_varying_size, bin_size = bin_size)
}
DT::datatable(obs_window,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','how the window is divided when bin size varies'),options = list(pageLength = 10) )
sprintf("bin size 500bp, number of training= %s, number of testing = %s",sum(obs_window$set_500 =="training"),sum(obs_window$set_500 =="testing"))
[1] "bin size 500bp, number of training= 43, number of testing = 7"
sprintf("bin size 1000bp, number of training= %s, number of testing = %s",sum(obs_window$set_1000 =="training"),sum(obs_window$set_1000 =="testing"))
[1] "bin size 1000bp, number of training= 48, number of testing = 2"
sprintf("bin size 2000bp, number of training= %s, number of testing = %s",sum(obs_window$set_2000 =="training"),sum(obs_window$set_2000 =="testing"))
[1] "bin size 2000bp, number of training= 44, number of testing = 6"
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt_smooth.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt_gammaonly.rdata")
sprintf("estimated alpha = %s", unique(df_per_window_data$alpha_est))
[1] "estimated alpha = 0.770703125"
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "Gamma prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_chr_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_chr_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_chr_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_chr_halftt.rdata")
df_per_window_data$sum_pred <- df_per_window_data$randeff_est*df_per_window_data$rl_rescaled_sum
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "sum_pred", ycol = "obs_sum",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_sum", ycol = "obs_sum",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

We partition each window into 1kb bins, use odd bins to train the local effect, and test on the even bins
fit = ebps(df_seg_non0$obs_train,df_seg_non0$rl_rescaled_train,smooth_control = list(wave_trans='ndwt',ndwt_method='smash'), general_control = list(verbose=T,printevery=1, maxiter=50))
df_per_window_data$test_pred <- df_per_window_data$randeff_est_train*df_per_window_data$rl_rescaled_test
load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_chr_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_chr_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_chr_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_chr_halftt.rdata")
data <- df_per_window_data[complete.cases(df_per_window_data$test_pred),]
DT::datatable(data,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;','Details for each window'),options = list(pageLength = 5) )
p1 <- scatter_plot_mutct(data = data, xcol = "test_pred", ycol = "obs_test",title = "smashgen prediction")
p2 <- scatter_plot_mutct(data = data, xcol = "rl_rescaled_test", ycol = "obs_test",title = "Roulette baseline")
grid.arrange(p1, p2, ncol = 2)

df_seg <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_halftt.rdata"))
df_chr <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_10kb_fitted_testpred_chr_halftt.rdata"))
df_compare <- data.frame(seg_est = df_seg$randeff_est,
chr_est = df_chr$randeff_est,
fold_change = df_seg$obs_sum/df_seg$randeff_est,
window_start = df_seg$Window_Start)
ggplot(data = df_compare) +aes(x=seg_est,y=chr_est) +
geom_point() +
labs(x = "random effects estimated from partitioned segments",
y= "random effects estimated from whole chromosome") +
geom_abline(slope = 1, intercept = 0, col="red") +
theme_minimal()

ggplot(df_compare, aes(x = window_start/1000000)) +
geom_line(aes(y = seg_est, color = "estimated from partitioned segments"), alpha = 0.4) +
geom_line(aes(y = chr_est, color = "estimated from whole chr"), alpha = 0.3) +
geom_line(aes(y = fold_change, color = "observed fold change"), alpha = 0.1) +
labs(x = "gemonic position (mb)",
y = "estimated random effects") +
scale_color_manual(name = "Group",
values = c("estimated from partitioned segments" = "blue",
"estimated from whole chr" = "green", "observed fold change" = "red"),
labels = c("estimated from partitioned segments","estimated from whole chr", "observed fold change")) +
theme_minimal()

df_seg <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_halftt.rdata"))
df_chr <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_30kb_fitted_testpred_chr_halftt.rdata"))
df_compare <- data.frame(seg_est = df_seg$randeff_est,
chr_est = df_chr$randeff_est,
fold_change = df_seg$obs_sum/df_seg$randeff_est,
window_start = df_seg$Window_Start)
ggplot(data = df_compare) +aes(x=seg_est,y=chr_est) +
geom_point() +
labs(x = "random effects estimated from partitioned segments",
y= "random effects estimated from whole chromosome") +
geom_abline(slope = 1, intercept = 0, col="red") +
theme_minimal()

ggplot(df_compare, aes(x = window_start/1000000)) +
geom_line(aes(y = seg_est, color = "estimated from partitioned segments"), alpha = 0.4) +
geom_line(aes(y = chr_est, color = "estimated from whole chr"), alpha = 0.3) +
geom_line(aes(y = fold_change, color = "observed fold change"), alpha = 0.1) +
labs(x = "gemonic position (mb)",
y = "estimated random effects") +
scale_color_manual(name = "Group",
values = c("estimated from partitioned segments" = "blue",
"estimated from whole chr" = "green", "observed fold change" = "red"),
labels = c("estimated from partitioned segments","estimated from whole chr", "observed fold change")) +
theme_minimal()

df_seg <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_halftt.rdata"))
df_chr <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_50kb_fitted_testpred_chr_halftt.rdata"))
df_compare <- data.frame(seg_est = df_seg$randeff_est,
chr_est = df_chr$randeff_est,
fold_change = df_seg$obs_sum/df_seg$randeff_est,
window_start = df_seg$Window_Start)
ggplot(data = df_compare) +aes(x=seg_est,y=chr_est) +
geom_point() +
labs(x = "random effects estimated from partitioned segments",
y= "random effects estimated from whole chromosome") +
geom_abline(slope = 1, intercept = 0, col="red") +
theme_minimal()

ggplot(df_compare, aes(x = window_start/1000000)) +
geom_line(aes(y = seg_est, color = "estimated from partitioned segments"), alpha = 0.4) +
geom_line(aes(y = chr_est, color = "estimated from whole chr"), alpha = 0.3) +
geom_line(aes(y = fold_change, color = "observed fold change"), alpha = 0.1) +
labs(x = "gemonic position (mb)",
y = "estimated random effects") +
scale_color_manual(name = "Group",
values = c("estimated from partitioned segments" = "blue",
"estimated from whole chr" = "green", "observed fold change" = "red"),
labels = c("estimated from partitioned segments","estimated from whole chr", "observed fold change")) +
theme_minimal()

df_seg <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_halftt.rdata"))
df_chr <- get(load("/project/xinhe/xsun/mutation_rate/11.denovo_real_mssng/results/mssng_trost_per_window_chr1_100kb_fitted_testpred_chr_halftt.rdata"))
df_compare <- data.frame(seg_est = df_seg$randeff_est,
chr_est = df_chr$randeff_est,
fold_change = df_seg$obs_sum/df_seg$randeff_est,
window_start = df_seg$Window_Start)
ggplot(data = df_compare) +aes(x=seg_est,y=chr_est) +
geom_point() +
labs(x = "random effects estimated from partitioned segments",
y= "random effects estimated from whole chromosome") +
geom_abline(slope = 1, intercept = 0, col="red") +
theme_minimal()

ggplot(df_compare, aes(x = window_start/1000000)) +
geom_line(aes(y = seg_est, color = "estimated from partitioned segments"), alpha = 0.4) +
geom_line(aes(y = chr_est, color = "estimated from whole chr"), alpha = 0.3) +
geom_line(aes(y = fold_change, color = "observed fold change"), alpha = 0.1) +
labs(x = "gemonic position (mb)",
y = "estimated random effects") +
scale_color_manual(name = "Group",
values = c("estimated from partitioned segments" = "blue",
"estimated from whole chr" = "green", "observed fold change" = "red"),
labels = c("estimated from partitioned segments","estimated from whole chr", "observed fold change")) +
theme_minimal()

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] data.table_1.14.2 gridExtra_2.3 ggplot2_3.5.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.12 highr_0.9 pillar_1.9.0 compiler_4.2.0
[5] bslib_0.3.1 later_1.3.0 jquerylib_0.1.4 git2r_0.30.1
[9] workflowr_1.7.0 tools_4.2.0 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 lifecycle_1.0.4 tibble_3.2.1 gtable_0.3.0
[17] pkgconfig_2.0.3 rlang_1.1.2 cli_3.6.1 rstudioapi_0.13
[21] crosstalk_1.2.0 yaml_2.3.5 xfun_0.41 fastmap_1.1.0
[25] withr_2.5.0 dplyr_1.1.4 stringr_1.5.1 knitr_1.39
[29] htmlwidgets_1.5.4 generics_0.1.2 fs_1.5.2 vctrs_0.6.5
[33] sass_0.4.1 DT_0.22 tidyselect_1.2.0 rprojroot_2.0.3
[37] grid_4.2.0 glue_1.6.2 R6_2.5.1 fansi_1.0.3
[41] rmarkdown_2.25 farver_2.1.0 magrittr_2.0.3 whisker_0.4
[45] scales_1.3.0 promises_1.2.0.1 htmltools_0.5.2 colorspace_2.0-3
[49] httpuv_1.6.5 labeling_0.4.2 utf8_1.2.2 stringi_1.7.6
[53] munsell_0.5.0 crayon_1.5.1